Mapping Flood Extent and Frequency from Sentinel-1 Imagery during the Extremely Warm Winter of 2020 in Boreal Floodplains and Forests

Sipelgas, Liis; Aavaste, Age; Uiboupin, Rivo

doi:10.3390/rs13234949

Cited by 5 publications

(1 citation statement)

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“…Sentinel-1 SAR is a powerful remote sensing datasets to identify the flooding areas. At least several publications [9,10,11,12] demonstrate the application of Sentinel-1 SAR imagery in mapping floods in various regions and under different conditions. Sentinel-1 has several advantages over other remote sensing techniques for flood mapping, including all-weather capability, high spatial resolution, ability to penetrate vegetation, ability to detect water, multi-temporal data, and global coverage.…”

Section: Introductionmentioning

confidence: 99%

Formulating flood mitigation through public participation, remote sensing analysis, and flood genetic understanding: study case Samarinda City flood January 2020

Rachman,

Kurniawan,

Nugraha

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Samarinda, a city characterized by its unique topography and proximity to major rivers, has been grappling with recurring and escalating flood events. The frequency and severity of these floods have been well-documented in numerous past academic studies, underscoring the pressing need for innovative flood mitigation strategies. In response to this challenge, this study employs a multi-faceted approach by incorporating public participation, remote sensing analysis, and flood genetic understanding. To facilitate a comprehensive flood mitigation strategy, we initiated the collection of real-time reports from the public during flood events. The participation of the local community in this data collection process has provided critical insights into the dynamics of flood occurrence and its impacts. The integration of crowdsourced data has been instrumental in augmenting our understanding of the local flood patterns. In parallel, we harnessed the capabilities of the Sentinel-1 satellite to conduct remote sensing analysis, enabling us to identify and map inundated areas during specific flood events. This approach, utilizing Sentinel-1, has been validated in prior flood studies. Our research further combines these datasets, merging live reports with remote sensing flood mapping to detect and assess affected areas. We introduce a novel categorization system based on flood genetics, dividing the floodprone region into three distinct zones according to hydrogeological condition of each water catchment area. We divided the area into three zones based on its genetic. This model guides us to the innovative approach enriching our flood mitigation strategy based on those valuable insights. The culmination of our study is the formulation of tailored mitigation plans for each of the three identified flood genetic zones. Incorporating public participation, remote sensing, and flood genetic understanding, our study provides a holistic framework for flood mitigation in the flood-prone city of Samarinda. By harnessing the collective intelligence of the community, leveraging cutting-edge technology, and integrating advanced scientific concepts, we present a comprehensive approach to address and mitigate the ongoing flood challenges in the region.

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Section: Introductionmentioning

confidence: 99%